def get_action(self, model, inputs, goals):
assert self._session is not None
feed_dict = {}
for name, ph in self._obs_placeholders.get_leaf_items():
value = np.array(inputs.get_recursive(name))
if value.shape == tuple():
value = value[np.newaxis]
feed_dict[ph] = value
for name, ph in self._goal_placeholders.get_leaf_items():
feed_dict[ph] = np.array(goals.get_recursive(name))
feed_dict[self._mppi_mean_placeholder] = self._mppi_mean_np
get_action_tf = {}
for name, tensor in self._get_action_outputs.get_leaf_items():
get_action_tf[name] = tensor
get_action_tf_output = self._session.run(get_action_tf,
feed_dict=feed_dict)
get_action = AttrDict()
for name, value in get_action_tf_output.items():
get_action.add_recursive(name, value)
self._mppi_mean_np = get_action.mppi_mean
return get_action
def call(self, inputs, obs_lowd=None, training=False):
if obs_lowd is None:
obs_lowd = [None] * len(self._models)
else:
obs_lowd = tf.split(obs_lowd, self._obs_lowd_dims, axis=-1)
outputs = AttrDict()
for model_i, obs_lowd_i in zip(self._models, obs_lowd):
outputs_i = model_i(inputs, obs_lowd=obs_lowd_i, training=training)
for key, value in outputs_i.get_leaf_items():
outputs.add_recursive(key, value)
return outputs
def _split_action(self, action):
"""
:param action (AttrDict):
"""
d = AttrDict()
idx = 0
for name in self._env_spec.action_names:
dim = np.sum(self._env_spec.names_to_shapes.get_recursive(name))
value = action[..., idx:idx + dim]
d.add_recursive(name, value)
idx += dim
return d
def _get_done(self):
names = ['collision/any', 'gps/latlong', 'joy']
obs = AttrDict.from_dict(self._jackal_subscriber.get(names=names))
is_collision = obs.collision.any
is_close_to_goal = np.linalg.norm(
latlong_to_utm(self._goal_latlong) -
latlong_to_utm(obs.gps.latlong)) < 2.0
return is_collision or is_close_to_goal
def denormalize(self, inputs):
"""
:param inputs (AttrDict):
:return: AttrDict
"""
inputs_denormalized = AttrDict()
for key, value in inputs.get_leaf_items():
lower, upper = self.names_to_limits.get_recursive(key)
lower, upper = np.array(lower), np.array(upper)
mean = 0.5 * (lower + upper)
std = 0.5 * (upper - lower)
value_denormalized = value * std + mean
inputs_denormalized.add_recursive(key, value_denormalized)
return inputs_denormalized
def _get_position_outputs(self, preprocess_outputs, inputs):
assert preprocess_outputs.shape.as_list()[-1] == 3
if self._is_output_gps:
batch_size = tf.shape(preprocess_outputs)[0]
position = tf.zeros([batch_size, 3])
yaw = -inputs.imu.compass_bearing[:,
0] + 0.5 * np.pi # so that east is 0 degrees
else:
position = inputs.jackal.position
yaw = inputs.jackal.yaw[:, 0]
output_positions = rotate_to_global(curr_position=position,
curr_yaw=yaw,
local_position=preprocess_outputs)
outputs = AttrDict()
outputs.add_recursive('jackal/position', output_positions)
return outputs
def _get_goal(self, obs):
goal_utm = latlong_to_utm(self._goal_latlong)
goal_utm -= obs.gps.utm
goal_utm = np.append(goal_utm, 0.)
cost_weights = rospy.get_param(
'/cost_weights', {
'collision': 1.0,
'position': 0.0,
'action_magnitude': 0.001,
'action_smooth': 0.0,
'bumpy': 0.8,
'position_sigmoid_center': 0.6,
'position_sigmoid_scale': 100.
})
for key, weight in cost_weights.items():
cost_weights[key] = np.ravel(weight).astype(np.float32)
return AttrDict(position=goal_utm,
cost_weights=AttrDict.from_dict(cost_weights))
def _preprocess_action_inputs(self, inputs):
### separate out actions
action_inputs = inputs.filter_recursive(
lambda key, value: key in self._env_spec.action_names)
# normalization
action_inputs.modify_recursive(lambda arr: tf.cast(arr, tf.float32))
normalized_inputs = self._env_spec.normalize(action_inputs)
actions = AttrDict()
for key in self._env_spec.action_names:
value = normalized_inputs.get_recursive(key)
if len(value.shape) == 2:
value = value[..., tf.newaxis]
elif len(value.shape) == 3:
pass
else:
raise ValueError
actions.add_recursive(key, value)
return actions
def get_batch(self, batch_size, horizon):
if self._static_inputs_and_outputs is None:
self._static_inputs_and_outputs = self._iterator.get_next()
inputs_and_outputs = self._static_inputs_and_outputs
inputs = AttrDict()
outputs = AttrDict()
for key, value in inputs_and_outputs.items():
if key.startswith('inputs/'):
inputs.add_recursive(key.replace('inputs/', ''), value)
else:
outputs.add_recursive(key.replace('outputs/', ''), value)
return inputs, outputs
def _get_outputs(self, preprocess_outputs, inputs, denormalize=True):
"""
Split the outputs into each prediction component and denormalize
:param preprocess_outputs (tensor): [batch_size, horizon, dim]
:return: AttrDict
"""
### split and denormalize
outputs_denormalized = AttrDict()
start_idx = 0
for output_observation in self._output_observations:
name = output_observation.name
shape = self._env_spec.names_to_shapes.get_recursive(name)
assert len(shape) == 1, 'Can only predict vector quantities'
dim = shape[0]
outputs_slice_denormalized = preprocess_outputs[
..., start_idx:start_idx + dim]
outputs_denormalized.add_recursive(name,
outputs_slice_denormalized)
start_idx += dim
outputs = self._env_spec.denormalize(
outputs_denormalized) if denormalize else outputs_denormalized
### make relative
for output_observation in self._output_observations:
name = output_observation.name
is_relative = output_observation.is_relative
if is_relative:
value = outputs.get_recursive(name)
value += inputs.get_recursive(name)[:, tf.newaxis, :]
outputs.add_recursive(name, value)
return outputs
def _preprocess_observation_inputs(self, inputs):
### separate out observations
obs_inputs = inputs.filter_recursive(
lambda key, value: key in self._env_spec.observation_names)
# normalization
obs_inputs.modify_recursive(lambda arr: tf.cast(arr, tf.float32))
normalized_inputs = self._env_spec.normalize(obs_inputs)
obs_ims = AttrDict()
obs_vecs = AttrDict()
for key, value in normalized_inputs.get_leaf_items():
value = normalized_inputs.get_recursive(key)
if len(value.shape) == 1:
obs_vecs.add_recursive(key, value[:, tf.newaxis])
elif len(value.shape) == 2:
obs_vecs.add_recursive(key, value)
elif len(value.shape) == 4:
obs_ims.add_recursive(key, value)
else:
raise ValueError
return obs_ims, obs_vecs
def _get_observation(self):
obs_names = set(self.spec.observation_names)
obs_names.add('gps/utm')
obs = AttrDict.from_dict(self._jackal_subscriber.get(names=obs_names))
if 'images/rgb_left' in obs.get_leaf_keys():
obs.images.rgb_left = self.spec.process_image(
'images/rgb_left', obs.images.rgb_left)
if 'images/rgb_right' in obs.get_leaf_keys():
obs.images.rgb_right = self.spec.process_image(
'images/rgb_right', obs.images.rgb_right)
obs.modify_recursive(lambda v: np.asarray(v))
return obs
def __init__(self, names_shapes_limits_dtypes):
names_shapes_limits_dtypes = list(names_shapes_limits_dtypes)
names_shapes_limits_dtypes += [('done', (1, ), (0, 1), np.bool)]
self._names_to_shapes = AttrDict()
self._names_to_limits = AttrDict()
self._names_to_dtypes = AttrDict()
for name, shape, limit, dtype in names_shapes_limits_dtypes:
self._names_to_shapes.add_recursive(name, shape)
self._names_to_limits.add_recursive(name, limit)
self._names_to_dtypes.add_recursive(name, dtype)
def _setup_mppi_graph(self, model, goals):
### create placeholders
obs_placeholders = AttrDict()
for name in self._env_spec.observation_names:
shape = list(self._env_spec.names_to_shapes.get_recursive(name))
dtype = tf.as_dtype(
self._env_spec.names_to_dtypes.get_recursive(name))
ph = tf.placeholder(dtype, shape=shape, name=name)
obs_placeholders.add_recursive(name, ph)
goal_placeholders = AttrDict()
for name, value in goals.get_leaf_items():
goal_placeholders.add_recursive(
name,
tf.placeholder(tf.as_dtype(value.dtype),
shape=value.shape,
name=name))
mppi_mean_placeholder = tf.placeholder(
tf.float32,
name='mppi_mean',
shape=[model.horizon, self._action_dim])
### get obs lowd
inputs = obs_placeholders.apply_recursive(
lambda value: value[tf.newaxis])
obs_lowd = model.get_obs_lowd(inputs)
past_mean = mppi_mean_placeholder[0]
shifted_mean = tf.concat(
[mppi_mean_placeholder[1:], mppi_mean_placeholder[-1:]], axis=0)
# sample through time
delta_limits = 0.5 * (self._action_selection_upper_limits -
self._action_selection_lower_limits)
eps = tf.random_normal(mean=0,
stddev=self._sigma * delta_limits,
shape=(self._N, model.horizon,
self._action_dim))
actions = []
for h in range(model.horizon):
if h == 0:
action_h = self._beta * (shifted_mean[h, :] + eps[:, h, :]) + (
1. - self._beta) * past_mean
else:
action_h = self._beta * (shifted_mean[h, :] + eps[:, h, :]) + (
1. - self._beta) * actions[-1]
actions.append(action_h)
actions = tf.stack(actions, axis=1)
actions = tf.clip_by_value(
actions, self._action_selection_lower_limits[np.newaxis,
np.newaxis],
self._action_selection_upper_limits[np.newaxis, np.newaxis])
# forward simulate
actions_split = self._split_action(actions)
inputs_tiled = inputs.apply_recursive(
lambda v: tf.tile(v, [self._N] + [1] * (len(v.shape) - 1)))
for k, v in actions_split.get_leaf_items():
inputs_tiled.add_recursive(k, v)
obs_lowd_tiled = tf.tile(obs_lowd, (self._N, 1))
### call model and evaluate cost
model_outputs = model(inputs_tiled, obs_lowd=obs_lowd_tiled)
model_outputs = model_outputs.filter_recursive(
lambda key, value: key[0] != '_')
costs_per_timestep = self._cost_fn(inputs_tiled, model_outputs,
goal_placeholders, actions_split)
costs = tf.reduce_mean(costs_per_timestep.total, axis=1)
# MPPI update
scores = -costs
probs = tf.exp(self._gamma * (scores - tf.reduce_max(scores)))
probs /= tf.reduce_sum(probs) + 1e-10
new_mppi_mean = tf.reduce_sum(actions *
probs[:, tf.newaxis, tf.newaxis],
axis=0)
best_idx = tf.argmin(costs)
best_actions = self._split_action(new_mppi_mean)
get_action_outputs = AttrDict(
cost=costs[best_idx],
cost_per_timestep=costs_per_timestep.apply_recursive(
lambda v: v[best_idx]),
action=best_actions.apply_recursive(lambda v: v[0]),
action_sequence=best_actions,
model_outputs=model_outputs.apply_recursive(lambda v: v[best_idx]),
all_costs=costs,
all_costs_per_timestep=costs_per_timestep,
all_actions=actions_split,
all_model_outputs=model_outputs,
mppi_mean=new_mppi_mean,
)
for key, value in get_action_outputs.get_leaf_items():
get_action_outputs.add_recursive(
key, tf.identity(value, 'get_action_outputs/' + key))
return obs_placeholders, goal_placeholders, mppi_mean_placeholder, get_action_outputs
class EnvSpec(object):
def __init__(self, names_shapes_limits_dtypes):
names_shapes_limits_dtypes = list(names_shapes_limits_dtypes)
names_shapes_limits_dtypes += [('done', (1, ), (0, 1), np.bool)]
self._names_to_shapes = AttrDict()
self._names_to_limits = AttrDict()
self._names_to_dtypes = AttrDict()
for name, shape, limit, dtype in names_shapes_limits_dtypes:
self._names_to_shapes.add_recursive(name, shape)
self._names_to_limits.add_recursive(name, limit)
self._names_to_dtypes.add_recursive(name, dtype)
@property
def observation_names(self):
raise NotImplementedError
@property
def output_observation_names(self):
return self.observation_names
@property
def action_names(self):
raise NotImplementedError
@property
def names(self):
return self.observation_names + self.action_names
@property
def names_to_shapes(self):
return self._names_to_shapes
@property
def names_to_limits(self):
return self._names_to_limits
@property
def names_to_dtypes(self):
return self._names_to_dtypes
def dims(self, names):
return np.array([
np.sum(self.names_to_shapes.get_recursive(name)) for name in names
])
def dim(self, names):
return np.sum(self.dims(names))
def normalize(self, inputs):
"""
:param inputs (AttrDict):
:return: AttrDict
"""
inputs_normalized = AttrDict()
for key, value in inputs.get_leaf_items():
lower, upper = self.names_to_limits.get_recursive(key)
lower, upper = np.array(lower), np.array(upper)
mean = 0.5 * (lower + upper)
std = 0.5 * (upper - lower)
value_normalized = (value - mean) / std
inputs_normalized.add_recursive(key, value_normalized)
return inputs_normalized
def denormalize(self, inputs):
"""
:param inputs (AttrDict):
:return: AttrDict
"""
inputs_denormalized = AttrDict()
for key, value in inputs.get_leaf_items():
lower, upper = self.names_to_limits.get_recursive(key)
lower, upper = np.array(lower), np.array(upper)
mean = 0.5 * (lower + upper)
std = 0.5 * (upper - lower)
value_denormalized = value * std + mean
inputs_denormalized.add_recursive(key, value_denormalized)
return inputs_denormalized
def process_image(self, name, image):
"""
Default behavior: resize the image
"""
if len(image.shape) == 4:
return np.array([self.process_image(name, im_i) for im_i in image])
return imresize(image, self.names_to_shapes.get_recursive(name))
def _process_inputs(self, inputs):
"""
Separate out observations/actions and normalize
:param inputs (AttrDict):
:return: obs_ims (AttrDict), obs_vecs (AttrDict), actions (AttrDict)
"""
### separate out observations/actions
# normalization
inputs.modify_recursive(lambda arr: tf.cast(arr, tf.float32))
normalized_inputs = self._env_spec.normalize(inputs)
obs_ims = AttrDict()
obs_vecs = AttrDict()
for key in self._env_spec.observation_names:
value = normalized_inputs.get_recursive(key)
if len(value.shape) == 1:
obs_vecs.add_recursive(key, value[:, tf.newaxis])
elif len(value.shape) == 2:
obs_vecs.add_recursive(key, value)
elif len(value.shape) == 4:
obs_ims.add_recursive(key, value)
else:
raise ValueError
actions = AttrDict()
for key in self._env_spec.action_names:
value = normalized_inputs.get_recursive(key)
if len(value.shape) == 2:
value = value[..., tf.newaxis]
elif len(value.shape) == 3:
pass
else:
raise ValueError
actions.add_recursive(key, value)
return obs_ims, obs_vecs, actions
